{"cells":[{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":["\n","import numpy as np\n","from scipy import stats\n","import pyutils.fig as fg\n","import STmodel.data.cases as stc\n","import STmodel.model.st as stm\n","import STmodel.model.stuq as stu\n"]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[],"source":["\n","case = stc.case.CaseSet('Aspden_2017_methane')\n"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[],"source":["\n","nx = 50\n","ny = 200\n","\n","ur_list = np.linspace(0.1, 20, num=nx)\n","lr = 1.0\n","\n","sr_list = np.zeros(nx)\n","\n","sr_ave = np.zeros(nx)\n","sr_std = np.zeros(nx)\n","\n","sr_p = np.linspace(0, 40, num=ny)\n","\n","pdf = np.zeros((nx, ny))\n"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[],"source":["\n","A_s, B_s, T_s, C_s = stu.sample_params(1000)\n"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[],"source":["\n","config = fg.PlotConfig('manuscript_single')\n"]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[],"source":["\n","unburnt = 'T-298_p-1_phi-0.7'\n","data = case.get_case_data(unburnt)\n","\n","r = case.get_reactant(unburnt)\n","\n","mixture = stm.Model(r)\n"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[],"source":["\n","for i, ur in enumerate(ur_list):\n","    # calculate model predicitons\n","    sr_list[i] = mixture.ratio_turbulent_burning_velocity(ur, lr)\n","    # calculate uncertainty\n","    sr_MC = stu.fwd_params(ur, lr,\n","                           unburnt,\n","                           case.get_fuel_stream(),\n","                           case.get_oxy_stream(),\n","                           case.get_mechanism(),\n","                           A_s, B_s, T_s, C_s)\n","\n","    kde = stats.gaussian_kde(sr_MC)\n","    pdf_ur = kde(sr_p)\n","    pdf[i] = pdf_ur\n","\n","    sr_ave[i] = np.average(sr_MC)\n","    sr_std[i] = 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\n"},"metadata":{}}],"source":["\n","fig, ax = config.get_simple()\n","\n","ax.errorbar(data.turbulence_intensity,\n","            data.turbulent_burning_velocity,\n","            yerr=data.turbulent_burning_velocity_std,\n","            fmt='o', c='b', mec='k', ms=6, mew=1,\n","            capsize=3, capthick=1.5, elinewidth=1,\n","            label='DNS')\n","\n","ax.plot(ur_list, sr_list, '-r', label='Model')\n","\n","ax.fill_between(ur_list, sr_ave - 2.0 * sr_std, sr_ave + 2.0 * sr_std,\n","                color='xkcd:light grey')\n","ax.fill_between(ur_list, sr_ave - sr_std, sr_ave + sr_std,\n","                color='xkcd:grey')\n","\n","ax.set_xlim(0, 20)\n","ax.set_ylim(0, 8)\n","\n","ax.set_xticks(np.linspace(0, 20, num=5))\n","ax.set_yticks(np.linspace(0, 8, num=5))\n","\n","ax.legend(frameon=False, loc='upper left')\n","\n","ax.text(6,0.4,\n","        'Aspden {\\it{et al.,}} 2017\\n'\n","        +r'CH$_4$/Air, $p=1\\;\\mathrm{atm}$, $\\phi=0.7$')\n","\n","ax.text(10,7.28,r'$l_t/\\delta_L^0=1$')\n","\n","ax.tick_params(which='major', direction='in', bottom=True, top=True, left=True, right=True)\n","\n","ax.set_xlabel(r'$u^\\prime/s_L^0$')\n","ax.set_ylabel(r'$s_T/s_L^0$')\n"]}],"nbformat":4,"nbformat_minor":2,"metadata":{"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.10-final"},"orig_nbformat":2,"kernelspec":{"name":"python3","display_name":"Python 3.7.10 64-bit ('ct': conda)","metadata":{"interpreter":{"hash":"8841c981494f636eca25c4e4fb03bbc12b88a62ffce3992df729a115722a659a"}}}}}